Description Usage Arguments Details Value Author(s) References Examples
View source: R/unbiasedPoint.R
unbiasedKrige
is a function for modifying a kriging prediction
to a prediction that can be assumed to be unbiased for a certain threshold.
1 2 3  unbiasedKrige(object, formulaString, observations, predictionLocations,
model, outputWhat, yamamoto, iwqmaxit = 500,
iwqCpAddLim = 0.0001, debug.level, ...)

object 
either an object of the intamap type (see 
formulaString 
formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name z, for ordinary and simple kriging use the formula z~1; for universal kriging, suppose z is linearly dependent on x and y, use the formula z~x+y 
observations 
a 
predictionLocations 
the predictionLocations, only necessary if the method is "IWQSEL" and formulaString contains independent variables. Should preferentally be a grid if the method is "IWQSEL" 
model 
variogram model of dependent variable (or its residuals), defined
by a call to 
outputWhat 
Argument with type of unbiasedness method ("MOK" or "IWQSEL") and the thresholds. 
yamamoto 
logical describing if the yamamoto approach )is to be used in simulations.
Defaults to yamamoto = FALSE when object is a 
iwqmaxit 
maximum number of iterations in iwqsel 
iwqCpAddLim 
convergence criteria in iwqsel 
debug.level 
debug level, passed to subfunctions 
... 
other arguments that will be passed to subfunctions. These include

It is a fact that predictions from kriging tend to be biased towards the mean of
the process. The function unbiasedKrige
is a function that adds one or more predictions
to the original output, which are assumed to be unbiased relative to a certain
threshold. The two methods supported are the IWQSELmethod (Craigmile, 2006) and
MOK (Skoien et al, 2008).
an object of type intamap, as described in intamappackage
, or a
Spatial
*DataFrame with one or more new prediction columns, representing different
methods and thresholds.
Jon Olav Skoien
Craigmile, P. F., N. Cressie, T. J. Santner, and Y. Rao. 2006. A loss function approach to identifying environmental exceedances. Extremes, 8, 143159.
Skoien, J. O., G. B. M. Heuvelink, and E. J. Pebesma. 2008. Unbiased block predictions and exceedance probabilities for environmental thresholds. In: J. Ortiz C. and X. Emery (eds). Proceedings of the eight international geostatistics congress. Gecamin, Santiago, Chile, pp. 831840.
Pebesma, E., Cornford, D., Dubois, G., Heuvelink, G.B.M., Hristopulos, D., Pilz, J., Stohlker, U., Morin, G., Skoien, J.O. INTAMAP: The design and implementation f an interoperable automated interpolation Web Service. Computers and Geosciences 37 (3), 2011.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  library(automap)
library(gstat)
data(meuse)
data(meuse.grid)
coordinates(meuse) = ~x+y
gridded(meuse.grid) = ~x+y
predictionLocations = meuse.grid[sample(1:length(meuse.grid),50),]
vmod = autofitVariogram(log(zinc)~1,meuse)$var_model
prediction = krige(log(zinc)~1,meuse,predictionLocations,vmod)
summary(prediction)
prediction < unbiasedKrige(prediction,log(zinc)~1,
meuse, model = vmod, outputWhat = list(MOK = 6.0, MOK = 7.0, IWQSEL=7.0),
iwqmaxit = 100, iwqCpAddLim = 0.01)
summary(prediction)

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